Real-time prediction of tumor motion using a dynamic neural network

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the motion of the target tumor. Real-time repositioning of the treatment beam during radiotherapy requires a method for predicting the tumor motion in ord...

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Bibliographic Details
Published in:Medical & biological engineering & computing 2020-03, Vol.58 (3), p.529-539
Main Authors: Mafi, Majid, Moghadam, Saeed Montazeri
Format: Article
Language:English
Subjects:
Biomedical and Life Sciences
Biomedical Engineering and Bioengineering
Biomedicine
Computer Applications
Computer Systems
Human Physiology
Humans
Imaging
Latency
Models, Theoretical
Motion
Neural networks
Neural Networks, Computer
Original Article
Radiation
Radiation dosage
Radiation therapy
Radiology
Real time
Retraining
Signal Processing, Computer-Assisted
Thorax
Tumors
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Summary:Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the motion of the target tumor. Real-time repositioning of the treatment beam during radiotherapy requires a method for predicting the tumor motion in order to overcome the inherent electro-mechanical latency of the radiotherapy equipment. Thus, besides respiratory motion, system latency also affects the accuracy of dose delivery. To compensate for the latency, a predictor should be employed to anticipate the position of the tumor and give some time to the radiotherapy system for repositioning the radiation beam. This study investigated the ability of spatio-temporal and dynamic neural networks in predicting tumor displacement caused by respiration. Nine different designs of neural networks with 665-ms prediction horizon were examined. The most accurate result was obtained using a dynamic 35-to-3 neural network which resulted in a mean absolute error of 0.54 ± 0.13 and a root mean square error of 0.57 ± 0.20. Moreover, the proposed predictor model is independent of any time-consuming processes such as real-time retraining and real-time baseline shift averaging. The results are comparable or superior with the current literature in terms of prediction accuracy. Graphical Abstract
ISSN:0140-0118
1741-0444
DOI:10.1007/s11517-019-02096-6